scholarly journals Rotating Elements Fault Diagnosis Method Based on Stochastic Resonance with Triple-well Potential System of Genetic Algorithm

2020 ◽  
Author(s):  
Liu ZiWen ◽  
Bao JinSong ◽  
Xiao Lei ◽  
Wang BoBo
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Engineering Application ◽  
Resonance Method ◽  
Rotating Body ◽  
Potential System ◽  
Diagnosis Method

Abstract In engineering applications, the fault signal of rotating elements is easily submerged in the background noise. In order to solve this problem, a rotating body fault diagnosis method based on stochastic resonance with triple-well potential system of genetic algorithm is proposed. In this method, the signal-to-noise ratio(SNR) of the Triple-well potential system is used as the fitness function of the genetic algorithm, and several parameters of the system are optimized at the same time, which effectively improves the feature extraction effect of the weak fault of the rotating body. The results of simulation and engineering experiments show that this method has better detection effect than bistable stochastic resonance method, and can effectively detect the fault signal submerged by noise, which has a good engineering application prospect.

Research on Stochastic Resonance Method Based on Bee Colony Algorithm and its Application to Bearing Fault Diagnosis

2014 ◽  
Vol 548-549 ◽  
pp. 374-378 ◽  
Author(s):  
Xiang Huan Cui ◽  
Yong Ying Jiang ◽  
Hai Feng Gao ◽  
Jia Wei Xiang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Parameters Selection ◽  
Input Signals ◽  
Diagnosis Method ◽  
System Output

The background noise makes it difficult to detect incipient faults through vibration analysis. The stochastic resonance (SR) method can be applied to enhance the signal-to-noise ratio (SNR) of a system output using the unavoidable environmental noise. The parameters selection is the most important to generate SR. The proposed fault diagnosis method utilizes the artificial bee colony algorithm to find the best parameters of SR so as to match input signals and detect faults. The performance of the proposed method is confirmed as compared to the fixed parameters method.

scholarly journals A New Second-Order Tristable Stochastic Resonance Method for Fault Diagnosis

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 965 ◽  
Author(s):  
Lu Lu ◽  
Yu Yuan ◽  
Heng Wang ◽  
Xing Zhao ◽  
Jianjie Zheng
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Fitness Function ◽  
Resonance Method ◽  
Poor Performance ◽  
Rolling Bearing ◽  
Second Order ◽  
Weak Signals ◽  
Rolling Bearings ◽  
Sr Method

Vibration signals are used to diagnosis faults of the rolling bearing which is symmetric structure. Stochastic resonance (SR) has been widely applied in weak signal feature extraction in recent years. It can utilize noise and enhance weak signals. However, the traditional SR method has poor performance, and it is difficult to determine parameters of SR. Therefore, a new second-order tristable SR method (STSR) based on a new potential combining the classical bistable potential with Woods-Saxon potential is proposed in this paper. Firstly, the envelope signal of rolling bearings is the input signal of STSR. Then, the output of signal-to-noise ratio (SNR) is used as the fitness function of the Seeker Optimization Algorithm (SOA) in order to optimize the parameters of SR. Finally, the optimal parameters are used to set the STSR system in order to enhance and extract weak signals of rolling bearings. Simulated and experimental signals are used to demonstrate the effectiveness of STSR. The diagnosis results show that the proposed STSR method can obtain higher output SNR and better filtering performance than the traditional SR methods. It provides a new idea for fault diagnosis of rotating machinery.

scholarly journals Application of continuous potential function stochastic resonance in early fault diagnosis of rolling bearings

2020 ◽  
Vol 53 (5-6) ◽  
pp. 767-777
Author(s):  
Xueping Ren ◽  
Jian Kang ◽  
Zhixing Li ◽  
Jianguo Wang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Potential Function ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Signal To Noise ◽  
Rolling Bearings ◽  
Continuous Potential ◽  
Potential Parameters

The early fault signal of rolling bearings is very weak, and when analyzed under strong background noise, the traditional signal processing method is not ideal. To extract fault characteristic information more clearly, the second-order UCPSR method is applied to the early fault diagnosis of rolling bearings. The continuous potential function itself is a continuous sinusoidal function. The particle transition is smooth and the output is better. Because of its three parameters, the potential structure is more comprehensive and has more abundant characteristics. When the periodic signal, noise and potential function are the best match, the system exhibits better denoise compared to that of other methods. This paper discusses the influence of potential parameters on the motion state of particles between potential wells in combination with the potential parameter variation diagrams discussed. Then, the formula of output signal-to-noise ratio is derived to further study the relationships among potential parameters, and then the ant colony algorithm is used to optimize potential parameters in order to obtain the optimal output signal-to-noise ratio. Finally, an early weak fault diagnosis method for bearings based on the underdamped continuous potential stochastic resonance model is proposed. Through simulation and experimental verification, the underdamped continuous potential stochastic resonance results are compared with those of the time-delayed feedback stochastic resonance method, which proves the validity of the underdamped continuous potential stochastic resonance method.

scholarly journals Research and application of weak fault diagnosis method based on asymmetric potential stochastic resonance

2019 ◽  
Vol 52 (5-6) ◽  
pp. 625-633 ◽  
Author(s):  
Zhixing Li ◽  
Boqiang Shi ◽  
Xueping Ren ◽  
Wenyan Zhu
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Bearing Failure ◽  
Asymmetric System ◽  
Symmetric Potential ◽  
Frequency Detection ◽  
Detection Technology ◽  
Potential Structure

Because fault characteristics are often difficult to extract from a strong noise background, it is essential for mechanical fault diagnosis to extract a weak characteristic signal with a very small signal-to-noise ratio from a noisy interference. Therefore, this paper proposes a new method for diagnosing weak faults in asymmetric potential stochastic resonance. Compared with the existing methods, the asymmetric potential stochastic resonance method not only has characteristics common to the symmetric potential stochastic resonance, but can also change the inclination of the barrier and slope of the wall to obtain a better model structure. The proposed method solves the local adjustment problem of the existing method from the perspective of potential structure and optimizes the asymmetric system shape to better target frequency detection during much interference from noise. After simulation, a bearing failure test, and rolling mill gearbox bearing failure experiments, we concluded that the asymmetric potential stochastic resonance detection technology can effectively identify faults. Compared with the symmetric potential stochastic resonance method, the proposed method has better recognition.

scholarly journals Time-Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Zhixing Li ◽  
Songjiu Han ◽  
Jianguo Wang ◽  
Xueping Ren ◽  
Chao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Delayed Feedback ◽  
Resonance Phenomenon ◽  
Steady State Probability ◽  
Resonance Model ◽  
Potential System ◽  
Feedback Strength ◽  
Diagnosis Method ◽  
Time Delayed Feedback

Pulses caused by rotating mechanical faults are weak and often submerged in strong background noise, which can affect the accuracy of fault detection. To solve this problem, we study the stochastic resonance phenomenon of a tristable potential system based on strong noise background and also investigate the influence of time-delayed feedback on this stochastic resonance model. The effects of time-delayed feedback strength on potential energy, steady-state probability density function, and signal-to-noise ratio (SNR) are discussed. The results show that stochastic resonance can be enhanced or suppressed by adjusting the delay time and feedback strength. Combined with bearing fault diagnosis simulation research and experimental verification evaluation, the proposed time-delayed feedback tristable stochastic resonance fault diagnosis method is more effective than the classical stochastic resonance method.

Effect of scale-varying fractional-order stochastic resonance by simulation and its application in bearing diagnosis

2019 ◽  
Vol 10 (01) ◽  
pp. 1941003 ◽  
Author(s):  
Kuo Chi ◽  
Jianshe Kang ◽  
Xinghui Zhang ◽  
Fei Zhao
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Fractional Order ◽  
Signal To Noise Ratio ◽  
Economic Losses ◽  
Integration Step ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Weak Fault

Bearing is among the most widely used components in rotating machinery. Its failure can cause serious economic losses or even disasters. However, the fault-induced impulses are weak especially for the early failure. As to the bearing fault diagnosis, a novel bearing diagnosis method based on scale-varying fractional-order stochastic resonance (SFrSR) is proposed. Signal-to-noise ratio of the SFrSR output is regarded as the criterion for evaluating the stochastic resonance (SR) output. In the proposed method, by selecting the proper parameters (integration step [Formula: see text], amplitude gain [Formula: see text] and fractional-order [Formula: see text]) of SFrSR, the weak fault-induced impulses, the noise and the potential can be matched with each other. An optimal fractional-order dynamic system can be generated. To verify the proposed SFrSR, numerical tests and application verification are conducted in comparison with the traditional scale-varying first-order SR (SFiSR). The results prove that the parameters [Formula: see text] and [Formula: see text] affect the SFrSR effect seriously and the proposed SFrSR can enhance the weak signal while suppressing the noise. The SFrSR is more effective for bearing fault diagnosis than SFiSR.

scholarly journals Adaptive Multiscale Noise Control Enhanced Stochastic Resonance Method Based on Modified EEMD with Its Application in Bearing Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Jimeng Li ◽  
Jinfeng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Noise Control ◽  
Engineering Application ◽  
Resonance Method ◽  
Structural Defects ◽  
Mechanical Equipment ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

The structure of mechanical equipment becomes increasingly complex, and tough environments under which it works often make bearings and gears subject to failure. However, effective extraction of useful feature information submerged in strong noise that is indicative of structural defects has remained a major challenge. Therefore, an adaptive multiscale noise control enhanced stochastic resonance (SR) method based on modified ensemble empirical mode decomposition (EEMD) for mechanical fault diagnosis is proposed in the paper. According to the oscillation characteristics of signal itself, the algorithm of modified EEMD can adaptively decompose the fault signals into different scales and it reduces the decomposition levels to improve calculation efficiency of the proposed method. Through filter processing with the constructed filters, the orthogonality of adjacent intrinsic mode functions (IMFs) can be improved, which is conducive to enhancing the extraction of weak features from strong noise. The constructed signal obtained by using IMFs is inputted into the SR system, and the noise control parameter of different scales is optimized and selected with the help of the genetic algorithm, thus achieving the enhancement extraction of weak features. Finally, simulation experiments and engineering application of bearing fault diagnosis demonstrate the effectiveness and feasibility of the proposed method.

Research and application of Stochastic Resonance in Quad-stable Potential System

2021 ◽  
Author(s):  
Li-Fang He ◽  
Qiu-Ling Liu ◽  
Tian-Qi Zhang
Keyword(s):  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
First Passage Time ◽  
Engineering Application ◽  
Reference Value ◽  
Passage Time ◽  
Signal To Noise ◽  
Stationary Probability Distribution ◽  
Potential System ◽  
Practical Engineering

Abstract To solve the problem of low weak signal enhancement performance in the quad-stable system, a new Quad-stable potential Stochastic Resonance (QSR) is proposed. Firstly, under the condition of adiabatic approximation theory, the Stationary Probability Distribution (SPD), the Mean First Passage Time (MFPT), the Work (W) and the power Spectrum Amplification Factor (SAF) are derived, and the impacts of system parameters on them are also deeply analyzed. Secondly, numerical simulations are performed to compare QSR with the Classical Tri-stable Stochastic Resonance (CTSR) by using the Genetic Algorithm (GA) and the fourth-order Runge-Kutta algorithm. It shows that the Signal-to-Noise Ratio (SNR) and Mean Signal-to-Noise Increase (MSNRI) of QSR are higher than CTSR, which indicates that QSR has superior noise immunity than CTSR. Finally, the two systems are applied in the detection on real bearing faults. The experimental results show that QSR is superior to CTSR, which provides a better theoretical significance and reference value for practical engineering application.

scholarly journals Bearing Fault Diagnosis Using Synthetic Quantitative Index-Based Adaptive Underdamped Stochastic Resonance

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Baochen Li ◽  
Rui Tong ◽  
Jianshe Kang ◽  
Kuo Chi
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Correlation Coefficient ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Quantitative Index ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Better Than

Stochastic resonance is like a nonlinear filter to detect the weak bearing fault-induced impulses that submerged in strong noises. Signal-to-noise ratio (SNR) is often used as the index to evaluate the SR output, but the fault characteristic frequency (FCF) must be known in order to calculate SNR. A novel bearing fault diagnosis method called synthetic quantitative index-based adaptive underdamped stochastic resonance (SQI-AUSR) is proposed. The synthetic quantitative index (SQI) is composed of power spectrum kurtosis, kurtosis, margin index, and correlation coefficient. The SQI is independent of FCF, which avoids the limitation that the calculation of SNR must know the FCF. Numeric simulations and two case studies of bearing faults are carried out. The results show that (1) the SQI is more effective than other proposed indexes such as correlation coefficient and weight power spectrum kurtosis and (2) the proposed SQI-AUSR is effective for bearing fault diagnosis and is better than SNR-AOSR.

Adaptive Stochastic Resonance Method for Early Fault Detection of Rotating Machinery Based on Alpha Stable Distribution

2014 ◽  
Vol 618 ◽  
pp. 458-462
Author(s):  
Gang Yu ◽  
Ye Chen
Keyword(s):  
Fault Detection ◽  
Stochastic Resonance ◽  
Stable Distribution ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Rotating Machinery ◽  
Sliding Windows ◽  
Alpha Stable Distribution ◽  
Early Fault Detection ◽  
The Impact

This paper proposes an adaptive stochastic resonance (SR) method based on alpha stable distribution for early fault detection of rotating machinery. By analyzing the SR characteristic of the impact signal based on sliding windows, SR can improve the signal to noise ratio and is suitable for early fault detection of rotating machinery. Alpha stable distribution is an effective tool for characterizing impact signals, therefore parameter alpha can be used as the evaluating parameter of SR. Through simulation study, the effectiveness of the proposed method has been verified.

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